SessionsNew Developments in Cost of
Injury

The Cost of Road Trauma:
Single and Multiple Injury Cases

Introduction

Casualties involved in road crashes often sustain multiple injuries, yet very
little is known about the cost of single injury compared with multiple injury
cases. The most common method of dealing with multiple injury cases in road
safety research is to allocate a primary injury to casualties on the basis of
the injury with the highest severity level. Subsequent analysis of the
distribution and cost of road injury is then based on the classification of the
primary injury.

The purpose of this study was to explore the cost of road trauma involving
single and multiple injury cases. More specifically, the study was conducted to
examine the marginal cost of each additional injury sustained by casualties in
road crashes. The work is currently in progress, and this paper presents some
preliminary results. Information relating to the impact of second and subsequent
injuries on the cost of a single injury is to be used in cost-effectiveness
analyses of interventions such as vehicle design changes, which may prevent the
occurrence of some, but not all, injuries sustained by motor vehicle occupants
in a crash.

Road Injury Cost Database

The cost data used in this study was obtained from the Road Injury Cost
Database. This database was constructed primarily from the unit records of
personal injury claims paid to road crash casualties in New South Wales,
Australia between July 1989 and June 1996. Finalized payments were available for
49,755 claimants. Personal injury insurance payments to individuals in the
claims database were recorded for the following cost categories: legal and
investigation, long term and home care, home and vehicle modifications, aids and
appliances, economic loss and general damages. Methods were developed to
allocate the following person-based costs of road injury to claimant records:
medical, hospital, rehabilitation, ambulance, future unpaid earnings, losses to
non-victims and personal injury insurance administration. Crash-based costs –
namely property damage, travel delay and motor vehicle insurance – are included
in the Road Injury Cost Database but were not used for the analyses in this
study.

Each claimant in the Road Injury Cost Database has up to five injuries coded
on the basis of the 1985 revision of the Abbreviated Injury Scale (AIS), with
lower extremity injuries divided into lower and upper leg injuries. A primary
injury was allocated to claimants on the basis of the injury with the highest
severity, and this classification was used for some comparisons of the cost of
single and multiple injury cases. If a claimant sustained two or more injuries
of the same maximum injury severity level, priority was assigned on the basis of
the following ordering: head, spine, lower extremities, thorax, abdomen, upper
extremities, neck, face and external (MacKenzie, Shapiro and Siegel, 1988).

Incidence of Single and Multiple Injury
Cases

Approximately one third of cases in the Road Injury Cost Database had a
single injury (n=16 603) and two thirds had multiple injuries (n=33 152). The
most commonly occurring single injuries were a minor injury (i.e. AIS 1) to the
neck (n=11 021), the spine (n=8 151) and the external body regions (n=3 683). On
the basis of the primary injury classification, the most frequently occurring
multiple injuries were a minor injury to the spine (n=7 283), the upper leg (n=2
764) and the neck (n=2 462).

Average Cost of Single and Multiple Injury
Cases

Tables 1 and 2 show the average cost by injury severity level for cases with
single and multiple injuries. For both single and multiple injury cases, costs
increased with the level of injury severity. For minor, moderate and serious
injuries, the cost of cases with multiple injuries was between 50%-80% greater
than the cost of cases with a single injury. However for severe and critical
injury, cases with a single injury had a higher cost than cases with multiple
injuries. This is a surprising result, which needs to be further explored.

TABLE 1 Average Cost of
Road Crash Casualties with Single and Multiple Injuries by Injury
Severity LevelAverage Cost by Level of Injury Severity
($000)

Number of Injuries

Minor(AIS 1)

Moderate(AIS 2)

Serious(AIS 3)

Severe(AIS 4)

Critical(AIS 5)

All(AIS 6)

Single

11.7

28.7

61.0

243.6

860.4

17.3

Multiple

21.4

44.9

107.6

189.3

616.8

42.7

All Cases

17.4

41.7

101.2

192.6

646.0

34.2

Cost differences by body region between single and multiple injury cases were
generally in the expected direction, with the exception of spinal injuries,
where cases with multiple injuries had a slightly lower average cost than those
with a single injury. Body regions for which cases with multiple injuries had
considerably greater costs than those with a single injury were the abdomen (3.3
times higher), the thorax (3.1 times higher) and the face (2.4 times higher).

TABLE 2Average Cost of Road
Crash Casualties with Single and Multiple Injuries by Body Region of
InjuryAverage Cost by Body Region of Injury
($000)

Body Region

Single Injury

Multiple Injuries

All Cases

Head

68.2

78.7

77.8

Spine

42.4

40.4

40.7

Upper Leg

33.0

53.5

50.6

Lower Leg

31.4

52.4

47.8

Thorax

11.0

34.2

30.5

Abdomen

16.1

52.6

47.2

Upper Extremity

18.8

31.5

28.6

Neck

11.2

19.2

13.0

Face

16.2

39.1

32.8

External

12.9

21.6

16.5

All

17.3

42.7

34.2

Two Models of the Cost of Single and
Multiple Injury Road Trauma Cases

Simple Additive/Non-Additive Model

A simple model of the cost of single and multiple injury cases was developed
using the cost of single injury cases as the basis to derive the expected cost
of related multiple injury cases. For example, this model predicts the cost of
cases with injuries of minor severity to the neck and spine from the actual cost
of (i) cases with a single injury of minor severity to the neck and (ii) cases
with a single injury of minor severity to the spine.

In this simple model, some cost categories – the additive costs – were
attributed to each individual injury sustained by multiple injury cases. For
these additive costs, the relevant costs for single injury cases were added
across the different injuries sustained by multiple injury cases to give an
aggregate of the additive cost categories. The following cost categories were
designated as additive: hospital, medical, rehabilitation, home and vehicle
modifications, and aids and appliances.

The remaining cost categories were designated as non-additive. For each
non-additive cost category, the largest cost for each body region of injury for
single injury cases was assumed to be the appropriate cost for multiple injury
cases. For example, if economic loss was greater for single injury cases with a
minor spinal injury than for single injury cases with a minor neck injury, then
the economic loss cost category for single injury cases with a spinal injury was
attributed as the appropriate cost for multiple injury cases with injuries of
minor severity to the neck and spine. Figure 1 presents the general equation for
the simple additive/non-additive model and a specific example.

FIGURE 1General Equation and Specific
Example of Simple Additive/Non-Additive Model of the Cost of Single and
Multiple Injury Cases

[maximum general damages for moderate head injury and minor spinal injury
cases] +

……. etc., etc.

Generalized Linear Model

The second approach to exploring the costs of single and multiple injury
cases involved developing a generalized linear model (GLM) of road injury costs
(the dependent variable) based on the body regions and injury severity levels of
injuries sustained (the independent variables). The body regions of injury were
entered in the model as class variables, with indicators used to denote
different injury severity levels. The model did not impose any order on the
indicators. Figure 2 presents the general equation for the model, together with
part of the fitted model and a specific example. The fitted model explained 36
percent of the variation in the total cost of injuries in the Road Injury Cost
Database.

FIGURE 2General Equation of the
Generalized Linear Model of the Cost of Road Crash Casualties with
Multiple Injuries

Comparison of the Predicted and Actual Costs
of Multiple Injuries Using the Two Models

Table 3 compares costs for selected multiple injury combinations derived from
the two models with the equivalent actual costs in the Road Injury Cost
Database. For example, the actual average cost of cases with a minor head and
minor neck injury was $18,920, while the costs predicted by the simple
additive/non-additive model and the GLM model were $21,640 and $17,990
respectively. In general, the GLM costs were closer to the actual costs in the
Road Injury Cost Database than those predicted by the simple
additive/non-additive model, although this was not always the case (e.g. the
simple model gave a closer fit for minor thorax/minor neck multiple injury
cases).

Discussion

This paper has presented some preliminary results of the relationship between
single injury and multiple injury road trauma cases. These results suggest that
further work is warranted in developing the generalized linear model of the cost
of injury based on the body region and injury severity level of injuries
sustained. Further work will involve including interaction terms in the model as
well as possibly some other refinements. In addition, the Road Injury Cost
Database is being extended by the addition of unit records between July 1996 and
June 1999. This latter data set will be used to test the relevance of the
refined Generalized Linear Model of the cost of road injuries.

References

Costs of Injury in Europe and the
Netherlands

Saakje MulderWillem Jan MeerdingEd van Beeck

Information on costs is important for setting priorities for research and
prevention in the field of accident-related injuries. This kind of information
forms an important addition to the data on the incidence and mortality rate of
accident-related injuries that is already recorded.

Hence, a project was launched with the explicit aim of developing a
computerized model which could calculate at any given moment the direct medical
costs (i.e. the costs within the healthcare sector) of injuries suffered in the
Netherlands. One of the conditions was that a uniform method be applied to
calculate the costs of the various injury categories (traffic, occupational,
home and leisure, sports, violence and self-mutilation). The model should also
be able to estimate the costs of specific injury scenarios (e.g. one-sided
bicycle accidents, falls among the elderly, horse-riding accidents, facial
injuries due to violence etc.).

The methodology was developed by the Working Group on the Costs of Injury.
The European Consumer Safety Association (ECOSA) organized several conferences
on the burden of injuries. During one such conference it was recommended to
establish an international working group which should specify standardized
methodologies for gathering appropriate information. ECOSA launched such a
Working Group in 1995, in which nine countries (twelve members) participated.
The composition of the Working Group is multidisciplinary, including health
economists, medical doctors, and epidemiologists. The aim of the Working Group
is to develop a tool to assess the societal costs of injuries in Europe as an
aid to priority setting and risk management decision making in injury control in
particular.

Three reports have been produced to date by the Working Group: a glossary, a
state-of-the art report, and a bibliography (all available on
http://www.ecosa.org/csi/ecosa.nsf). The Working Group also drafted a proposal
on the application of the model for the direct costs of injury within the
European Union. This proposal was submitted to the Injury Prevention Programme
of the European Commission (to be decided in August 2000).

Methodology and Data

The calculation model estimates the costs of all LIS-registered injuries,
i.e. injuries which are treated at the Emergency Departments of various
hospitals in the Netherlands (incidence-based approach). The model was applied
to data from 1997. The registered accident-related injuries were assigned to
patient groups that were differentiated according to certain characteristics
that determine the use and costs of healthcare. These characteristics were:
admittance/non-admittance to hospital, type of injury, injury severity, age, and
gender.

The information used by the model was derived from LIS, standard care
registrations, a supplementary survey of a random sample of LIS patients and
various sources of cost-price information.

The calculation method works as follows. For each cost element a LIS patient
is assigned to a specific patient group. The average costs per patient group are
then calculated for each cost element and the total costs of all the elements
are calculated for each LIS patient. Lastly, the total costs are determined by
adding up the costs of all the patients. This estimate of total costs can be
made for any selection of LIS patients.

Results

In 1997 the total direct costs of direct accident-related injuries came to
2.2 billion guilders and accounted for 3.4% of the total healthcare budget for
1997. In the same year 1.1 million injury patients were treated at Emergency
Departments and 104,000 patients were admitted to hospital.

Costs Borne by the Healthcare Sector

Almost three quarters (1.6 billion guilders) of the costs of injuries are
incurred in hospitals and rehabilitation hospitals. 50% of the total costs are
attributable to hospital and nursing home care, 30% to care in outpatient
departments (half of which is emergency assistance) and 20% to extramural and
other care. Compared with other types of illness the total costs spent on the
out-patient care of injuries is disproportionately high.

Costs According to Age and Sex

Though women sustain only 40% of the total number of injuries, they account
for 55% of the costs. This is mainly because many of the injuries suffered by
older women require a high level of care. Women account for a larger proportion
of hospital, nursing home and home care costs (65%) than men (35%).

The costs per injury patient increase with age. This increase is already
discernible among young adults and, together with a high incidence rate reaches
a clear peak in total costs for men in the 25-44 age group. The upward trend in
costs per injury patient becomes exponential in older age groups. Hence, people
aged 75 and over account for as much as 32% of the total medical costs of
injuries (702 million guilders) while they represent only 5% of the total number
of injuries sustained.

Costs by Injury

No less than 53% of all injuries are due to home and leisure accidents
(580,000 patients). Their share of the costs is even higher: 59% (1.3 billion
guilders). A large proportion of these injuries are hip fractures, which account
for as much as one third of the total cost of home and leisure accidents.

Traffic accidents are responsible for 13% of injuries and 19% of the medical
costs (410 million guilders). Sports accidents take third place at 10% (206
million guilders). These are followed by occupational injuries (112 million
guilders, 5%), violence (53 million guilders, 2,5%) and self-mutilation (45
million guilders, 2%).

Costs by Type of Injury

Injuries to the lower extremities result in medical costs of almost 1 billion
guilders (45% of the total costs). This is high considering that they account
for only 17% of the total number of injuries. The costs per patient are
relatively low for superficial injuries, but an exceptionally high incidence
(30% of all injuries) leads to overall costs of 230 million guilders (11% of the
total). The greatest strain on the budget are hip fractures (468 million, 22%);
followed by superficial injuries (230 million, 11%); open wounds (149 million,
7%); fractures of the knee and lower leg (137 million, 6%); ankle fractures (96
million); and skull and brain injuries, with the exception of concussion (85
million, 4%).

Costs by Accident Scenario

The model must be able to calculate the costs of any selection of LIS
patients. This report contains a few examples of ‘accident scenarios’ which
provide insight into the costs of injury accruing from car accidents (101
million guilders); cycling accidents (165 million guilders); one-sided cycling
accidents (107 million guilders); poisoning incidents involving young children
(3 million guilders); fall-related hip fractures among the elderly (75+) (333
million guilders); do-it-yourself activities (24 million guilders); outdoor
football accidents (48 million guilders); horse-riding accidents (14 million
guilders; open wounds due to occupational accidents (18 million guilders);
facial injuries due to violence (10 million guilders); and suicide attempts by
poisoning (33 million guilders).

Potential and Limitations

When used in combination with LIS the cost model provides a coherent picture
of incidence, the healthcare use and the costs of acute physical injuries in the
Netherlands. As a concept, costs provide an easy-to-interpret public health
indicator that enables injury-patient data from diverse sources to be combined
and integrated under one common denominator: guilders.

The cost model can provide policy information for:

priority setting in injury prevention

the evaluation of policy measures

priority setting in public health as a whole

As far as policy-making is concerned, the added value of this cost model
rests primarily in the opportunities it provides for ongoing and detailed
monitoring of accident-related injuries. For example, it allows distinctions to
be made according to healthcare sector, age, gender, accident category and type
of injury. Policy can also be supported by detailed estimates of incidence,
healthcare use and the direct medical costs of specific accident scenarios (e.g.
one-sided cycling accidents). Thanks to a uniform methodology the cost estimates
can be effectively compared for all accident categories and healthcare sectors.
Hence, the cost model forms a sound basis for the evaluation of the costs and
effects of specific preventive measures.

However, when interpreting the results, it is important to take account of
certain limitations connected mainly with the availability of data. The model
will be refined and optimised in the future when additional and/or better data
becomes available. Accordingly, a maintenance plan has been compiled to ensure
that the model is kept up-to-date.

It is also the intention to further develop the model with estimates of the
indirect costs of injury due to sick leave and occupational disability, and
estimates of lost years of life and loss of quality of life due to limitations
and handicaps.

Conclusion

In this cost model, data from various sources is integrated and connected in
such a way that it results in combined information on injury prevention,
healthcare use and medical costs, and facilitates internal comparisons. Hence,
the model forms a new source of information for priority setting in injury
prevention policy and for the evaluation of policy measures.

van Beeck EF, Mulder S. Measuring the Costs of Injury in Europe: a
Review of the State-of-the-Art. Amsterdam: European Consumer Safety
Association; 1998.

The Valuation of Safety in the UK –
Some Recent Developments

David J. Ball

Introduction

In some sectors of the United Kingdom (UK) economy, proposals for safety
interventions are subject to cost-benefit tests prior to implementation, which
in turn is reliant upon monetary values being places upon lost lives and
injuries. In the transport sector, the currently used breakdown of costs per
casualty, based on a 1996 valuation of the benefits of the prevention of road
accidents and casualties is set out in Table 1. As can be seen, the largest
component of cost is in all cases that ascribed to ‘pain, grief and suffering.’
Valuation of this component has, for many years, been achieved by use of
contingent valuation.

Note: the
final column contains the DETR’s own published figures.1 The other
figures are estimates based on the DETR’s 1996 distribution of costs between
components.

In 1997 the Consumer Affairs and Competition Policy Directorate of the UK
Department of Trade and Industry (DTI) commissioned its own research on injury
valuation in the context of the management of risks in relation to consumer
product safety. The outcome of this research was to suggest an alternative
strategy for decision making which was later accepted.2 Essentially,
it was decided to adopt a range from £1million to £10 million for a statistical
life (for most purposes a narrower range of £2 million to £4 million would be
used) as a rule of thumb for guiding decisions about safety interventions.

At about the same time, several UK government departments, led by the UK
Health & Safety Executive (HSE), commissioned a further ambitious study of
the valuation of safety in different contexts, some of the results of which have
now been reported.3

This paper provides a brief account and commentary on these developments.

The Needs of the Consumer Sector

For many years the lead on injury valuation in Britain has been taken by the
Department of Transport (now the Department of the Environment, Transport and
the Regions - DETR). A great deal of pioneering work has been sponsored by them
using contingent valuation (CV) as the reference technique.4 Much of
this work has been carried out using increasingly sophisticated and specialist
economic tools without, it is probably fair to say, much opportunity for
cross-disciplinary or public scrutiny.

Elsewhere, in the broader field of risk management, the emphasis has been
moving rapidly away (in theory at least, but no doubt practice will follow) from
one of ‘the expert knows best’ towards a much more open and consultative process
in which stakeholders are encouraged to contribute directly in decision making.
Now, it has been argued that contingent valuation is, indirectly, a consultative
technique since it relies upon asking consumers how much they would be willing
to pay to avoid the ‘pain, grief and suffering’ of injury and death. What, on
the face of it, could be fairer than that? Unfortunately, this argument breaks
down should willingness to pay (WTP) fail in some way to measure consumer
desires.5 In such circumstances it could even subvert the democratic
decision process by masquerading as truth.

In the case of the DTI, it was clear that any use of safety valuation would
have to stand a reasonable chance of being acceptable and plausible to the
exceedingly diverse group of stakeholders with interests in consumer products
and services. Furthermore, consumer safety decision making has tended
historically to be a more open and consultative process than has typically been
the case in the road safety sector. The implication of this was that room was
always going to be necessary for negotiation.

Thus, a critical review was carried out for DTI of WTP and other techniques
for injury valuation from a multi-disciplinary perspective. This review
concluded that, so far as CV studies were concerned, there remained many
reservations about the technique, despite the years of research. In summary,
these included the following: people may not have clear pre-formed preferences
for non-market goods, and survey responses may therefore not be an accurate
measure of true preferences; the CV task may be too complex for respondents; the
scenario against which the CV task is performed may inadequately encapsulate the
issue with which the respondent is concerned; ‘embedding’ effects may occur if
the respondent does not clearly distinguish between subsets of a good and a good
in its entirety; people may ‘construct’ their preferences using the information
provided; respondents may be insensitive to the size of hypothetical risk
reductions; biases may be generated by the survey methodology; and so
on.6 To this list should be added the task of reconstructing a
societal value of safety from numerous individual WTP responses, itself a
process requiring additional non-trivial assumptions and value judgements.

What is Really Attainable or Necessary for
Risk Decisions?

Given that CV is usually regarded as the technique with the most going for it
as far as valuation of non-market goods is concerned, the above catalogue makes
depressing reading. Or does it? The answer to the question really depends on how
accurate you think the answer could be anyway, or is needed to be.

Regarding accuracy, I am reminded of my early days as a physicist when we
learnt that there was an absolute limit to the precision with which the position
and momentum of sub-atomic particles (electrons) could be determined,
irrespective of any future developments in measurement techniques.
Electrons apparently were inherently fuzzy objects and, like it or not, we would
have to live with it. The analogy for safety valuation is that, apart from
methodological problems, the variance observed in results from CV studies must
also contain a component attributable to the inherent imprecision or fuzziness
of the concept for human beings. On reflection, it is inconceivable that people
could hold anything approaching precise valuations of such entities as pain and
suffering. This fuzziness is tantamount to random background noise that is
impenetrable by further research, however cleverly conducted.

Thus, an alternative way of thinking about the value of a statistical life
(VOSL) would be to ask how precise a quantity it would be if a perfect research
instrument existed. The answer to such a question would then provide some
guidance on what further research was warranted. In the UK there has been a
little speculation about what the limit might be. In 1993 Jones-Lee, our most
eminent safety economist, suggested the limit might be an order of
magnitude.7 More recently, in a report following a UK
inter-departmental consultative process on the setting of safety standards, the
limit was placed at ± 50%,8 both of which values are of course quite
significant. Furthermore, a highly complex backdrop of both personal and
societal contextual factors that are constantly being reassessed by those
concerned compounds this ‘noise.’ Given the diversity of interest groups in the
consumer sector, any suggestion that an attempt to identify anything approaching
a high-precision valuation of safety would be a lost cause.

So what should be done? The choice recommended to the DTI, and subsequently
accepted, was to bite the bullet and acknowledge that VOSL was not a
well-defined quantity, and therefore to specify it as a range rather than a
point value. Although this created a ripple of surprise in some circles, this
choice in fact had significant advantages. First, it was more ‘scientific’ to be
open about uncertainty and hence more defensible; second, admission of
uncertainty was likely to make the whole concept more plausible so far as
consumer groups were concerned (cf. the 4 significant figures assigned to VOSL
in Table 1); third, specifying VOSL as a range is one way of providing decision
makers with room to manoeuvre in safety decisions. The pressing need for space
to negotiate and show sensitivity to stakeholder concerns is one of many lessons
which effective risk managers have learnt over past decades.9,10
Fourth, the upper limit of the range could be used to screen out rogue
decisions, which is arguably what the cost of safety debate has largely been
about anyway. So, paradoxically, defeat was turned at least in small measure
into victory.

There still remained the question of what the range for VOSL should be. The
review indicated that so far as human costs (pain, grief and suffering) were
concerned, the likely range was £0.5 million to £10 million. This was rounded up
to £1 million to £10 million to allow for lost output and other costs. It was
suggested that for most decisions DTI might use a more restricted range of from
£2 million to £4 million. Again, this caused a ripple of shock in some quarters
because it was felt to be ‘high’ compared with currently-accepted valuations
like that of £902,500 in Table 1. In defence of the higher value, several
factors are pertinent, one of which is the long lead-time before many consumer
safety decisions bite. For instance, measures on furniture safety may not fully
impact for two decades or more.

The HSE-Led Study

Regarding the HSE-led study, only the first phase of this has been reported,
so no more than a preliminary account is possible. The overall aim of the study
was to produce safety valuations for different hazard contexts, with the first
report dealing with road fatalities. The study was singularly noteworthy in the
extent to which pre-piloting and other measures were adopted in order to try to
deal with some of the known problems associated with CV.

In particular, the problem of lack of sensitivity of respondents to the size
of risk reduction available was carefully examined. In each of two phases of
piloting, this problem was found to be severe. For example, forty per cent of
respondents reported identical WTP for two risk reductions, one of which was
three times the other. This was studied using tape recordings of individual
interviews and follow-up focus group meetings. It emerged that the likely
reasons were: many people found the risk reductions tiny and marginalized them;
any safety improvement was seen as a good thing and the actual magnitude of risk
reduction was of secondary or no importance; that when considering how much the
‘good thing’ was worth, this was equated to what could easily be afforded –
usually £50 to £200 per annum. All of this confirms what has long been said
about CV.

In order to try to overcome these problems the authors resorted to various
sophisticated measures. This included the use of a combination of CV for a
non-fatal injury, a variant of standard gamble to elicit willingness to trade
off risk of non-fatal injury against death, and the linkage of these results to
infer the marginal rate of substitution for death. Even so, it is apparent from
the text that many heroic assumptions and value judgements were necessary in
order to turn the results into a value of safety for the avoidance of road
traffic fatalities. The final conclusion being that a range was appropriate, in
fact any figure from £750,000 to £1,250,000 could be regarded as ‘broadly
acceptable,’ a finding which of course is consistent with that in Table 1.
However, with this kind of research, in which it is customary to discard data
that does not fit the model, one encounters the danger of simply regenerating
previously held ideas. Such a thing has happened in physics. At one time there
was a remarkable consistency in measurements by different scientists of the
velocity of light. This continued until a radically different result was
reported, at which point all further measurements clustered around the new
value.

Apart from the methodological value judgements, other assumptions are
entrained within the procedure that would have substantial effects on the
outcome. To mention just one, those who are exposed to road traffic risks are
presumed to care only for themselves and not at all for others. Jones-Lee et
al.11 have suggested in the past that were an altruism factor
included, it alone might boost WTP for avoidance of a fatality by about £0.5
million (1983 prices). While belief in altruism may these days be at a low ebb
in CV circles, it is not universally so. As was written to The Times of London
in 1999 by the president of The Pedestrians Association (UK):

“But for most of us what counts is funerals per year – how many of
our sons, daughters, spouses, parents and neighbours die on the roads. We
should, furthermore, forecast these deaths. Let us assume that it will be
possible to cut road deaths by 5 per cent a year over the next decade. Between
1999 and 2009 that would still give us 24,000 funerals. Those deaths would be
family tragedies.”12

It is hard indeed to believe that individuals could be so uncaring as to
place zero value on others, and evidently this is not a position shared by the
Pedestrians Association. Given that the annual risk of death from road traffic
is about 1:8000 per year in industrialized countries, this suggests that all of
us will have a family member or close friend killed during our lifetime. As
remarked by Dorman, himself an economist though perhaps not a member of the
mainstream:

“We are all at risk of missing the most fundamental aspects of
life, things that should be obvious to us but are not. Academic economists
possess this trait in abundance”13

Personally I don’t wish to pick on economists in particular. It is well known
that experts of all persuasions have blind spots. That is why research such as
this, which can have a profound effect upon public safety, needs to be opened up
to as wide a scrutiny as possible.

References

Department of the Environment, Transport and the Regions. High
Econ. September 1998. Note. No.1.

National estimates of lifetime economic burdens associated with injury and
illness rely on problematic life table and productivity loss computations. In
part, these problems arise because the human capital cost method used to
estimate economic costs employs costing methods for work loss that were
developed in 1966 and then standardized in the early 1980s (Rice et al. 1966,
Hodgson and Meiners, 1982). Since the early 1980s, forensic economists have
found flaws in those methods and developed improved methods for use when
litigation requires valuing health-related work losses. This paper applies what
those economists have learned to suggest improvements in the classic approach
for valuing injury or disease burden.

The paper makes 11 points: (1) Most life expectancy tables provide a static
picture based on current health status in a population cross-section rather than
the life expectancy of population age cohorts. Since health is improving, such
tables underestimate actual life expectancy. (2) The true life expectancy for
serious injury victims is shorter than for the average population. (3) Work loss
costs have an appropriate place in a QALY-based cost-framework, albeit a limited
and possibly forced one. (4) There are two basic approaches for projecting
earnings loss due to injury or illness. Each approach has limitations and
neither produces perfect results. (5) The current methods assume that the
current national cross-section of wages by age and sex will be the pattern of
earnings for individuals as they age in the future. For sex based and education
based reasons, it is likely that the future will be different than the present.
(6) Too often, we use factors such as labor force participation and unemployment
rates that are dependent on the business cycle but apply the most recent year of
data rather than an average across the cycle. (7) For purposes of placing costs
on occupational injury, using growth rates based on economy-wide averages may
distort an analysis. (8) The merit of including the value of family services
lost in a WTP or QALY-based framework is an open question. (9) The services an
individual provides within the family extend far beyond simple production
relationships. If family services are valued separately, there should be more
consideration of the complex nexus of interrelationships that produce care,
support, guidance and counsel for all family members. The degree to which
different surveys of family services capture parts of this important complexity
varies enormously. (10) Death and catastrophic injury disrupt the lives of the
surviving family, affecting earnings, educational achievement, family services,
and quality of life. They can cause depression and other costly mental health
problems. Even QALY loss estimates generally fail to capture these impacts,
especially for fatalities. (11) When a worker is killed or permanently disabled,
society incurs costs beyond individual work losses because of the hiring,
training, and other costs of workers shifting between jobs. Such friction costs
have been measured in the Netherlands and should be assessed and properly
incorporated into the costing framework elsewhere.

Life Table Issues

What Life Table Should We Use?

Standard life tables have been cross-sectional. They give historical survival
probabilities of the current population by age group, sex, and race. For
example, the survival probability for someone age 61 might be the mean survival
probability from age 60 to 61 observed among people who now are age 63. This
approach implies that life expectancy is constant over time. However, the
current cohort that includes 10 year olds will have a very different profile of
education, early health behaviors, available health treatments, and
environmental exposures when they reach age 85 than does the current cohort that
includes people age 85. For this reason, the Board of Governors of the U.S.
Social Security Commission recently concluded that future retirement benefits
have been substantially underestimated. The Governors urged the Commission to
develop and adopt cohort life tables that project the survival probabilities of
people currently of a given age and gender, taking into account future
developments that can be reasonably anticipated. The cost of illness literature
needs to make a similar change to cohort based life expectancy tables. This
should be done with caution. Available life insurance actuarial tables are often
cohort-based, but they describe the expected life spans of insurance purchasers,
a risk adverse group with above-average survival probabilities.

A third type of life table has recently been developed in the U.S. (Robine et
al. 1991, 1992; Erickson et al. 1995, Expectancy Data 1999) and is needed
elsewhere. This type of life table focuses on expected years of healthy life.
For each age group by gender, this table is computed by multiplying the
probability of surviving another year from a cross-sectional or cohort life
table times the average health of people of the given age group and gender.
Average health levels are measured in quality-adjusted life years (QALYs). The
QALY data come from a national household survey like ones conducted recently in
the U.S., Canada, Netherlands, and the United Kingdom, among others. Correctly
estimating QALYs lost to premature death or permanent disability requires a
Healthy Life Expectancy (HLE) life table. HLE expectancy will always be smaller
than life expectancy due to health impairments associated with the aging
process. For example, a baby boy who suffocated had about a 75% chance of living
to age 66. That means his death cost an expected 0.75 years of life from age 66
to 67. Men age 66 have an average QALY level of 0.74 (Expectancy Data 1999), so
the corresponding QALY loss would be 0.75 * 0.74 = 0.555 years.

Do Catastrophic Injury Victims Have Typical Life Spans?

Injury is the leading cause of death from ages 1 to 45 in the U.S. and
accounts for more than half of deaths in some age groups. Injury’s dominant role
as a cause of death implies survival probabilities in a life table are sensitive
to fatal injury risk, especially at younger ages. The issue thus becomes, do
people who die from injury have atypical survival probabilities? We first answer
this question for alcohol-involved fatalities. High blood alcohol levels
significantly increase the probability of fatal or catastrophic injury, most
notably in traffic crashes. High blood alcohol levels also correlate with other
often-fatal future alcohol-linked morbidity such as cirrhosis and cancer.
Further, heavy drinkers have above-average probabilities of smoking, drugging,
driving aggressively, and failing to buckle up. These observations strongly
suggest that people who are fatally injured while having high blood alcohol
levels are risk-takers with elevated probabilities of premature death. Thus, if
these individuals had survived the injuries that killed them, they still would
have had life expectancies well below the average. In the United States, about
35% of fatal injuries involve persons with high alcohol blood levels (Smith et
al. 1999), even though only 11% of the US population are heavy drinkers. This
strongly suggests that the average pre-event life expectancy for people who died
from injury is substantially below the average for the population as a whole.
Yet we all use average life expectancies to compute lifetime medical costs,
productivity losses, and QALY losses. That means we overestimate the losses.

Work Loss Issues

Should Productivity Loss Be Explicit in Willingness to Pay (WTP) and QALY
Costs?

This section neither endorses nor disavows the use of WTP, a choice on which
the authors are divided. We agree that explicit work loss estimates are
informative and valuable regardless of the cost framework used. People want to
know about work losses and related tax losses, but including them with QALYs or
WTP estimates poses the risk of double counting. A WTP value of life or a QALY
estimate inherently includes all sources of value from the life process.
Therefore, its use with a separate wage loss estimate implies that wage loss has
been double counted. However, using only WTP or QALY estimates of injury burden
means that persons uncomfortable with using WTP measures or QALYs will be unable
to salvage cost estimates from the burden estimates.

One approach which Arthur (1980) and Miller, Calhoun, and Arthur (1989) claim
is appropriate is to subtract the work loss from the WTP estimate and show it as
a separate cost component. This allows analysts who use a WTP cost framework to
provide information on productivity losses without double counting. (Note that
the total cost simply is WTP.) Miller (1993) and subsequent studies took this
approach, arbitrarily naming the residual “pain, suffering, and lost quality of
life.” Using that terminology, “quality of life” encompasses life quality
reductions beyond those resulting from losing wages that funded consumption,
plus any residual value for being alive as compared to dead. Similarly and less
controversially, one could include the work losses in costs and separately show
a non-monetary estimate of QALYs lost. The QALY measure used should exclude
impacts on work-related functioning or be adjusted to remove those impacts. Even
with an appropriate QALY measure, it is unclear that QALY loss and wage loss can
be cleanly separated since quality of life comes, in part, from
earnings-dependent consumption.

How Do We Calculate Lifetime Productivity Loss: LPE versus WLE?

Two methods typically are used to compute lifetime productivity losses: LPE
(Life-Participation-Employment) and Work Life Expectancy (WLE). LPE and WLE
estimates typically differ by about 5%-6%. Choice between these methods can
depend on data availability or a strength or weakness in a specific application.
LPE has most often been used in national cost of injury estimates. The LPE
formula (with the sum to the oldest age the data permit) is:

The advantages and disadvantages of WLE and LPE are mirror images. WLE is
much easier to understand. With WLE, it is easier to tailor work life to
atypical groups and data more often are available for subgroups, for example, by
education or occupation. However, most uses of WLE assume that years are worked
consecutively, which is a serious problem when valuing losses for women of
childbearing age. Arbitrary methods exist to adjust the work life pattern to
reflect such considerations but data have not been compiled for a scientifically
sound adjustment. Another disadvantage of WLE tables is that they are updated
infrequently, while data for LPE calculations are updated annually.

Several problems arise in work loss estimation. Both LPE and WLE assume the
labor force participation probability is independent of prior participation. In
other words, they use identical probabilities of future participation for
situations in which all victims were employed when they died and in which very
few victims were employed when they died. An occasional mistake arises when
analysts use data for average earnings and then apply an unemployment percentage
to those earnings. In cases where average earnings data already include
unemployed persons, this results in at least partial double counting of the
probability of being unemployed. A more frequent error is the use of the most
recent values for P and E in analyzing lifetime losses. Since these factors vary
with the business cycle, P and E values should be based on averages over the
business cycle. These two problems are compounded if average earnings data that
includes unemployment is combined with current P and E values. The average
earnings values reflect the stage of the business cycle in a way that is
magnified when combined with contemporaneous participation and employment rates.
However, trying to average over the cycle is also dangerous. Uncertainty in the
lifetime estimates is large because economies often vary in unprecedented ways.
In the U.S. for example, the boom side of the business cycle appears to be
lengthening.

Both LPE and WLE assume injured persons are randomly drawn from the
population. However, the population of severely injured persons is not randomly
selected. Heavy drinking, for example, lowers expected earnings (Rice et al.
1990). Homicide and fatal fire victims are disproportionately low-income people.
Low-income babies are less likely to travel in child seats (Mayer and LeClere
1994), meaning babies killed in automobile crashes are likely to be low-income
babies. For any of these reasons, averages based on the population as a whole
can overestimate true losses. Averages also can underestimate true losses.
Drowning victims in swimming pools, for example, are likely to drown at home or
in a neighbor’s pool, which means that their families could afford swimming
pools. People killed in airline crashes could afford to fly. Side airbags are
only in newer, more expensive vehicles, so they are only likely to kill
high-income people. Anyone killed at work currently was employed, meaning that
she or he was not unemployed at the time. Studies that examine workplace
fatalities often focus on one occupation or industry, for example construction
where workers earn above-average wages. Unfortunately, estimating group-specific
participation rates, employment rates and earnings data is very difficult, but
accuracy is lost when this is not done.

Should Family Services and Housework Loss Be Evaluated?

Family services and housework losses certainly belong in an economic cost
estimate. These are not included in the Gross Domestic Product (GDP) in most
countries, which makes their inclusion in cost comparisons both difficult and
uncomfortable. Since there are no standardized methods for valuing these
services, cross-country comparisons are also hampered. It is debatable whether
family services should be valued separately in a QALY or WTP framework. A strong
argument against omitting them is that they discriminate against women,
especially full-time homemakers, who perform most of these services.

Empirically, the analyst valuing family services needs to thoughtfully
consider what time uses have been defined as “household services” in the
underlying time use study being used to measure family services. Sometimes,
“household services” are narrowly defined and omit many important educational
services by parents for children, for example. If educational services by
parents have not been accounted for in other ways, they should be included in
cost comparisons as part of projected family services. From our perspective,
family services such as care, support, guidance, counsel, and financial
management are important losses; they should not be ignored.

Should We Value Caregiver, Survivor and Other Family Costs of Catastrophic
Injury?

Especially when a child is the victim, catastrophic injury disrupts earnings,
education, and family duties of caregivers, other family, and friends. The
amount of needed family services rises significantly. The child’s injury drives
family members and friends into depression and other mental illness. It reduces
the quality of life for all family members in ways that may be partly captured
in willingness to pay measures but almost never is in QALY estimates (for an
exception, see Mohide et al. 1988). These losses can be substantial, but we
rarely account for them. We should.

What About Friction Costs?

Friction costs (Koopmanschapp et al. 1995) measure employer productivity
losses outside the family, specifically the costs that ripple through the
economy as employers hire and train permanent and temporary replacement workers
and juggle work schedules following a death or disabling injury. They belong in
WTP, QALY, and economic cost frameworks when estimating societal costs.

Summary

We should be using cohort life tables and, with QALYs, healthy life
expectancy.

The lack of tailored life tables for injury victims overestimates costs
and QALY losses.

Explicit productivity loss estimates are desirable even in WTP or QALY
frameworks.

Distinguishing average earnings of earners from earnings already adjusted
for unemployment is essential.

It is desirable to use multi-year average values for participation and
employment rates.

If practical, analysts should tailor participation, employment and
earnings rates to the group injured.

Family service losses should be computed thoughtfully.

Societal costs should include caregiver and survivor costs, as well as
worker replacement costs of employers.

Acknowledgement

This paper derives from work under National Institute on Occupational Safety
and Health grant R01/CCR312179 and Health Resources and Services Administration
Children’s Safety Network contract MCJ-240-98-0006. All opinions are the
authors’ alone.

References

Arthur WB. The Economics of Risks to Life. Laxenburg, Austria:
International Institute for Applied Systems Analysis; 1979.

Mayer M, LeClere FB. Injury prevention measures in households with
children in the United States, 1990. National Center for Health
Statistics; Advance Data. Vital and Health Statistics for the Center for
Disease Control and Prevention; 1994.

International Colleborative Effort on
Injury Statistics and Minimum Data Sets

Lois A. Fingerhut, M.A.

The International Collaborative Effort (ICE) on Injury Statistics is one of
several international activities sponsored by the National Center for Health
Statistics (NCHS) for the purpose of improving international comparability and
quality of injury data. The ICE serves to provide the data needed to better
understand the causes of injury and the most effective means of prevention and
to provide a forum for international experts to discuss data related issues. The
ICE focuses on injury definitions, data collection methodologies, coding and
classification. The ICE is comprised of representatives from about a dozen
countries including colleagues primarily from government, academia and injury
prevention and control units. The first meeting of the ICE was held in May 1994
and meetings have been held nearly annually since then. ICE receives funding
from the National Institute of Health’s National Institute of Child Health and
Human Development.

What is the ICECI?

ICECI is a classification tool for injury researchers and data collectors
that is being designed for data collection in emergency departments (referred to
also as accident and emergency or a&e). What makes ICECI unique is that it
is multi-axial, allowing one to capture many different dimensions of injury
(cause, intent, place, activity, drug use, and others) as separate codes rather
than the International Classification of Disease (ICD) which is one-dimensional,
forcing one code to capture several dimensions. The final version of ICECI is
scheduled to be released in October 2000. Primary responsibility for ICECI is
with the Consumer Safety Institute in Amsterdam, The Netherlands.

The core elements of ICECI are:

Object or substances producing the injury (injuries)

Mechanism (direct and precipitating)

Intent

Place where injury occurred

Activity injured was doing

Drug and alcohol use

Modules that are being developed include:

Violence (under development)

Relationship between victim and perpetrator

Context of assault

Precipitating factors for suicide attempt

Type of legal intervention

Transport (under development)

Mode of transport

Counterpart

User

Context (traffic, non-traffic, not a crash)

Sports (under development)

Occupation (yet to be developed)

In addition to the full ICECI, a short version is under development in the
United States as a routine surveillance tool for use in emergency departments.
This version was developed to be fully compatible with the full ICECI and with
the ICD-10 external cause of injury code sets. The short ICECI is being
developed at the National Center for Injury Prevention and Control at the
National Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia.

Minimum Basic Data Set(s) (MBDS)

In many parts of the world, the ICECI and even the short version of the ICECI
will not be a feasible tool for data collection. A real need was perceived to
develop a minimum set of data elements that could be collected even among the
most resource challenged environments. Toward that effort, a multi-national
group including representatives from the CDC, Norway, South Africa, Uganda,
India and the WHO have been working together to develop a manual to provide
injury researchers with the tools for establishing a surveillance system that
could be used at the most “basic” level.

Proposed MBDS

Identification number

Victim age and sex

Injury characteristics

Intent

Place and activity

Nature of injury

Mechanism of injury

Optional elements

Race and ethnicity

Residence of victim

Disposition

Narrative description of how injury occurred

Date and time of injury

Alcohol and other substance use

Severity of injury

In selecting a classification, be it the ICD, the ICECI, the short ICECI or
any other one, it is important to decide in what settings will it be used; i.e.,
clinics, emergency departments, inpatient settings or for death certification.
The setting can help determine the most appropriate choice. In addition, the
user should fully understand the limitations of whatever classification is
chosen – for example, does it rely only a single code for use; is it
multi-axial; are there guidelines for use.